Machine Learning Engineer

Our client's team seeks a Machine Learning Engineer to advance the development and improvement of our software foundation and tools vital for training state-of-the-art AI models. Your role will be centered on creating strong, scalable, and efficient training infrastructures and frameworks facilitating the full spectrum of the machine learning process, from handling data to deploying models. In collaboration with researchers and software engineers, you'll ensure that training systems are smoothly integrated and functioning, expanding the limits of AI's capabilities, especially in practical robotics scenarios. Additionally, you will investigate innovative methods to effectively utilize diverse datasets within our training framework.Responsibilities:Create and uphold efficient, scalable, and distributed training systems including data preprocessing, training orchestration, and model assessment for training large-scale AI models.Enhance the efficiency of training procedures to improve performance and use of resources, while maintaining scalability and dependability.Collaborate with researchers to create training and evaluation pipelines for state-of-the-art algorithms.Develop and design benchmarks for evaluating ML models.Perform training and fine-tuning of foundation models for robotic applications.Monitor and analyze pipelines, identifying bottlenecks and proposing solutions to improve efficiency and performance.Ensure the robustness and reliability of the training infrastructure, including automated testing and continuous integration.Preferred Qualifications:BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.Strong background in distributed computing, parallel processing techniques, handling large-scale datasets and data preprocessing.Deep understanding of state-of-the-art machine learning techniques and models.Experience with cloud-based training environments (AWS, Google Cloud, Azure).Experience in developing and maintaining software tooling and infrastructure for machine learning.Deep understanding and practical experience with software engineering principles, including algorithms, data structures, and system design.Experience with continuous integration and automated testing frameworks.

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